Methods and systems for analyzing brain lesions with longitudinal 3d mri data

ABSTRACT

Some methods of analyzing one or more brain lesions of a patient comprise, for each of the lesion(s), calculating one or more lesion characteristics from a first 3-dimensional (3D) representation of the lesion obtained from data taken at a first time and a second 3D representation of the lesion obtained from data taken at a second time that is after the first time. The characteristic(s) can include a change, form the first time to the second time, in the lesion&#39;s volume and/or surface area, the lesion&#39;s displacement from the first time to the second time, and/or the lesion&#39;s theoretical radius ratio at each of the first and second times. Some methods comprise characterizing whether the patient has multiple sclerosis and/or the progression of multiple sclerosis in the patient based at least in part on the calculation of the lesion characteristic(s) of each of the lesion(s).

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 17/031,010, filed Sep. 24, 2020, and claims benefit of U.S.Provisional Patent Application No. 62/905,079, filed Sep. 24, 2019, theentire contents of both which are incorporated herein by reference.

FIELD OF INVENTION

The present invention relates generally to diagnosing and assessing theprogression of multiple sclerosis in patients based on the analysis ofbrain lesions.

BACKGROUND

The diagnosis of multiple sclerosis (MS) is based on both clinical andradiological assessments of damage disseminated in both time and space.This may include a radiological assessment of whether a requisite numberof lesions in the periventricular, juxtacortical, infratentorial, andspinal cord regions have a specific character (e.g., size, shape, andmorphology) and spatial distribution patterns indicative of MS. Theeffective application of the existing dissemination in space criteriamay be hindered by the highly sensitive nature of magnetic resonanceimaging (MRI) technology, the heterogeneity of lesions resulting from avariety of medical conditions, concomitant radiological featuresresulting from age-related changes and disease, and the lack ofadditional radiological characteristics beyond two-dimensional (2D)descriptions.

Currently, MS diagnosis is typically performed using 2D MRI images. Theimplementation of certain imaging metrics, including the use ofquantitative phase imaging, has improved lesion specificity. This mayhighlight the presence of central vasculature within lesions anddistinct peripheral rings, suggesting the presence of iron withinmacrophages. The use of fluid-attenuated inversion recovery (FLAIR) MRIat 3 Tesla (T) and T2-weighted and susceptibility weighted imaging (SWI)at 7 T in larger patient groups has also been utilized to bettercharacterize MS from non-MS lesions. However, this technique has beenlimited by the lack of appreciation of the central vessel in allorthogonal planes of view and the abundance of vessels intersectinglesions within the supratentorial region. Beyond these efforts,peripheral regions of hypointensity, presumed to be related to thepresence of iron within macrophages, have also been described in MSpatients.

Due to the shortcomings of the current 2D MRI, anomalies associated withnon-specific white matter disease (NSWMD) may often be misinterpreted asMS lesions, resulting in the misclassification of patients even whencoupled with clinical and para-clinical assessments. Accordingly, thereis a need in the art for assessments that can more accuratelycharacterize MS and NSWMD patients.

SUMMARY

Some of the present methods and systems address this need in the artthrough the use of multiple 3-dimensional (3D) representations of brainlesions obtained at different points in time. Changes in the lesion'svolume, surface area, displacement, and/or shape over time can beassessed by comparing the 3D representations; the use of 3Drepresentations permits meaningful detection and quantification of thechanges, something that 2D MRI images—even those having a highresolution—typically cannot provide due to the limitations thereof. Thechanges in these parameters tend to be different for lesions associatedwith MS compared to those associated with NSWMD. For example, ascompared to NSWMD lesions, MS lesions generally experience changes involume and in surface area that are smaller and displacements that arelarger. Further, NSWMD lesions tend to maintain a more spherical shapeover time as compared to MS lesions. An assessment of criteria based onthese temporal changes may better be able to distinguish MS and NSWDlesions compared to cross-sectional assessments that analyze lesions atonly one point in time, yielding more accurate characterizations of thepatient's disease state. In this manner, the use of 3D representationsin conjunction with a temporal analysis can yield a more accuratecharacterization of MS and NSWMD compared to conventional techniques.

Some of the present methods of analyzing one or more lesions of a brainof a patient comprise, for each of the lesion(s), calculating one ormore lesion characteristics from a first 3D representation of the lesionobtained from data taken at a first time and a second 3D representationof the lesion obtained from data taken at a second time that is afterthe first time. Some methods comprise characterizing whether the patienthas multiple sclerosis and/or the progression of multiple sclerosis inthe patient based at least in part on the calculation of the lesioncharacteristic(s) of each of the lesion(s). Some methods comprise, foreach of the lesion(s), assessing whether one or more criteria aresatisfied, wherein characterizing whether the patient has multiplesclerosis and/or the progression of multiple sclerosis in the patient isbased at least in part on the assessment of the one or more criteria foreach of the lesion(s).

Some of the present systems comprise one or more processors configuredto, for each of the lesion(s), calculate one or more lesioncharacteristics from a first 3D representation of the lesion obtainedfrom data taken at a first time and a second 3D representation of thelesion obtained from data taken at a second time that is after the firsttime. The processor(s), in some systems, are configured to characterizewhether the patient has multiple sclerosis and/or the progression ofmultiple sclerosis in the patient based at least in part on thecalculation of the lesion characteristic(s) of each of the lesion(s). Insome systems, the processor(s) are configured to, for each of thelesion(s), assess whether one or more criteria are satisfied andcharacterize whether the patient has multiple sclerosis and/or theprogression of multiple sclerosis in the patient based at least in parton the assessment of the one or more criteria for each of the lesion(s).

In some embodiments, the one or more lesion characteristics include achange, from the first time to the second time, in the volume of thelesion; a change, from the first time to the second time, in the surfacearea of the lesion; a displacement of the lesion from the first time tothe second time; and/or the theoretical radius ratio of the lesion ateach of the first and second times. The one or more criteria, for someembodiments, include a volume-based criterion that is satisfied when thechange in volume of the lesion is less than or equal to a thresholdvolume change; an area-based criterion that is satisfied when the changein the surface area of the lesion is less than or equal to a thresholdsurface area change; a displacement-based criterion that is satisfiedwhen the change in the position of the lesion is greater than or equalto a threshold displacement; and/or a deformation-based criterion thatis satisfied when the theoretical radius ratio of the lesion at each ofthe first and second times is greater than or equal to a thresholdtheoretical radius ratio.

Some methods comprise determining the threshold volume change and/or thethreshold surface area change based at least in part on the age of thepatient, and in some systems the processor(s) are configured todetermine the threshold volume change and/or the threshold area changebased at least in part on the age of the patient. In some embodiments,the threshold volume change is less than or equal to 2.0 millimeters(mm), optionally less than or equal to 0.0 mm and/or the thresholdsurface area change is less than or equal to 1.5 mm, optionally lessthan or equal to 0.0 mm. In some methods, the threshold displacementand/or the threshold theoretical radius ratio are not determined basedon the age of the patient, and in some systems the processor(s) areconfigured to assess whether the displacement-based criterion and/or thedeformation-based criterion are satisfied without determining thethreshold displacement and/or the threshold theoretical radius ratiobased on the age of the patient. The threshold theoretical radius ratio,in some embodiments, is greater than or equal to 1.025, optionallygreater than or equal to 1.02505.

In some embodiments, the data taken at the first time and the data takenat the second time each is a 3D MRI image of the brain of the patient.The time elapsed between the first and second times, in someembodiments, is between 6 months and 4 years.

Some methods comprise determining for at least a majority of thelesion(s) that at least one of the one or more criteria is satisfied anddetermining that the patient has multiple sclerosis.

The terms “a” and “an” are defined as one or more unless this disclosureexplicitly requires otherwise. The terms “substantially” and “about”each is defined as largely but not necessarily wholly what isspecified—and includes what is specified; e.g., substantially or about90 degrees includes 90 degrees and substantially or about parallelincludes parallel—as understood by a person of ordinary skill in theart. In any disclosed embodiment, the terms “substantially” and “about”may each be substituted with “within [a percentage] of” what isspecified, where the percentage includes 0.1, 1, 5, and 10 percent.

The terms “comprise” and any form thereof such as “comprises” and“comprising,” “have” and any form thereof such as “has” and “having,”and “include” and any form thereof such as “includes” and “including”are open-ended linking verbs. As a result, an apparatus that“comprises,” “has,” or “includes” one or more elements possesses thoseone or more elements, but is not limited to possessing only thoseelements. Likewise, a method that “comprises,” “has,” or “includes” oneor more steps possesses those one or more steps, but is not limited topossessing only those one or more steps.

Any embodiment of any of the apparatuses, systems, and methods canconsist of or consist essentially of—rather thancomprise/include/have—any of the described steps, elements, and/orfeatures. Thus, in any of the claims, the term “consisting of” or“consisting essentially of” can be substituted for any of the open-endedlinking verbs recited above, in order to change the scope of a givenclaim from what it would otherwise be using the open-ended linking verb.

Further, a device or system that is configured in a certain way isconfigured in at least that way, but it can also be configured in otherways than those specifically described.

The feature or features of one embodiment may be applied to otherembodiments, even though not described or illustrated, unless expresslyprohibited by this disclosure or the nature of the embodiments.

Some details associated with the embodiments described above and othersare described below.

BRIEF DESCRIPTION OF THE DRAWINGS

The following drawings illustrate by way of example and not limitation.For the sake of brevity and clarity, every feature of a given structureis not always labeled in every figure in which that structure appears.Identical reference numbers do not necessarily indicate an identicalstructure. Rather, the same reference number may be used to indicate asimilar feature or a feature with similar functionality, as maynon-identical reference numbers.

FIG. 1 illustrates some of the present methods of analyzing one or morelesions of a brain using 3D MRI.

FIG. 2 is a schematic of a system that can be used to perform some ofthe present methods.

FIGS. 3A and 3B each shows a 2D MRI axial FLAIR image (left) and a 3DMRI sagittal FLAIR image (right) of a brain of a 49-year-old white womanwith relapsing remitting MS that includes multiple lesions. The imagesof FIG. 3A were obtained at a first time and the images of FIG. 3B wereobtained at a second time 1 year after the first time.

FIGS. 4A-4D each shows first and second 3D representations of one of thelesions of FIGS. 3A and 3B, the first 3D representation (empty mesh)representing the lesion at the first time and the second 3Drepresentation (filled mesh) representing the lesion at the second time.

FIG. 5A shows a first 3D representation of a brain lesion of an MSpatient obtained at a first time and the displacement vectorsillustrating how the representation's vertices moved from the first timeto a second time. The shading and size of the displacement vectorsrepresent the magnitude of displacement; as shown, the vectors arearranged asymmetrically.

FIG. 5B shows the first 3D representation of the brain lesion of FIG. 5A(empty mesh), the displacement vectors, and a second 3D representationof the brain lesion (filled mesh) representing the lesion at the secondtime.

FIG. 5C illustrates the displacement vectors of FIG. 5A.

FIG. 6A shows a first 3D representation of a brain lesion of an NSWMDpatient obtained at a first time and displacement vectors illustratinghow the representation's vertices moved from the first time to a secondtime. The shading and size of the displacement vectors represent themagnitude of displacement; as shown, the vectors are arranged relativelysymmetrically compared to the FIG. 5A vectors.

FIG. 6B shows the first 3D representation of the brain lesion of FIG. 6A(filled mesh), the displacement vectors, and a second 3D representationof the brain lesion (empty mesh) representing the lesion at the secondtime.

FIG. 6C illustrates the displacement vectors of FIG. 6A.

FIG. 7 shows a 2D MRI axial FLAIR image of a brain of a patient withrelapsing remitting MS. FIG. 7 also shows, for each of the lesions ofthe brain, a first 3D representation of the lesion taken at a first time(empty mesh) and a second 3D representation of the lesion taken at asecond time (filled mesh) that is approximately 1 year after the firsttime.

FIGS. 8A and 8B are 2D MRI axial FLAIR images of a brain of a42-year-old white woman having NSWMD, where the FIG. 8A image was takenat a first time and the FIG. 8B image was taken at a second time that is1 year after the first time. A single lesion is highlighted in each ofthe images.

FIGS. 9A-9D each shows first and second 3D representations of the lesionin FIGS. 8A and 8B, the first representation (filled mesh) representingthe lesion at the first time and the second representation (empty mesh)representing the lesion at the second time.

FIGS. 10A and 10B are trace plots for the statistical analysis of lesionvolume performed in Example 1.

FIGS. 10C-10E are plots used as posterior predictive checks of thedistribution of response, random effect distribution, and residualversus fitted values, respectively, for the statistical analysis oflesion volume performed in Example 1.

FIG. 10F is a plot showing posterior predictive error versus theobserved values for the statistical analysis of lesion volume performedin Example 1.

FIGS. 11A and 11B are trace plots for the statistical analysis of lesionsurface area performed in Example 1.

FIGS. 11C-11E are plots used as posterior predictive checks of thedistribution of response, random effect distribution, and residualversus fitted values, respectively, for the statistical analysis oflesion surface area performed in Example 1.

FIG. 11F is a plot showing posterior predictive error versus theobserved values for the statistical analysis of lesion surface areaperformed in Example 1.

FIGS. 12A and 12B are trace plots for the statistical analysis of lesiondisplacement performed in Example 1.

FIGS. 12C-12E are plots used as posterior predictive checks of thedistribution of response, random effect distribution, and residualversus fitted values, respectively, for the statistical analysis oflesion displacement performed in Example 1.

FIG. 12F is a plot showing posterior predictive error versus theobserved values for the statistical analysis of displacement performedin Example 1.

FIGS. 13A-13C are trace plots for the statistical analysis of lesiontheoretical radius ratio performed in Example 1.

FIGS. 13D-13G are plots used as a posterior predictive checks of thedistribution of response, patient-level random effect distribution,lesion-level random effect distribution, and residual versus fittedvalues, respectively, for the statistical analysis of lesion theoreticalradius ratio performed in Example 1.

FIG. 13H is a plot showing posterior predictive error versus theobserved values for the statistical analysis of lesion theoreticalradius ratio performed in Example 1.

DETAILED DESCRIPTION

Referring to FIGS. 1 and 2 , shown is an illustrative method 10 foranalyzing one or more brain lesions of a patient and a system 30configured to perform method 10. While some of the present methods aredescribed with reference to system 30, system 30 is not limiting onthose methods, which can be performed with any suitable system.

Some of the present methods include a step 14 of, for each of one ormore—optionally, a plurality of—lesions (e.g., 58) of a patient's brain(e.g., 54), obtaining a first 3D representation (e.g., 62 a) of thelesion from data taken at a first time and a second 3D representation(e.g., 62 b) of the lesion obtained from data taken at a second timethat is after the first time. For example, the data taken at the firsttime and the data taken at the second time can each be a 3D MRI image ofthe brain taken with an MRI device (e.g., 34) of system 30; for each ofthe 3D MRI images, the 3D representation of the lesion can be segmentedfrom the image for analysis. Any suitable time can be elapsed betweenthe first and second times; for example, the time elapsed between thefirst and second times can be greater than or equal to any one of, orbetween any two of, 3 months, 4 months, 5 months, 6 months, 1 year, 2years, 3 years, or 4 years (e.g., between 6 months and 4 years).

To obtain the first and second 3D representations of each of thelesion(s) from the 3D MRI images, system 30 can include a processingdevice (e.g., 38) having one or more processors configured to receiveeach of those 3D images and a segmentation application (e.g., 42) bywhich the processor(s) can segment the 3D MRI image into one or moreregions of interest (ROIs) that can each correspond to one of the brainlesion(s). An illustrative segmentation application is OsiriX fromPixmeo SARL of Geneva, Switzerland. The processing device can be a partof a computer system including standard components such as a hard drive,monitor, printer, keyboard, and mouse, and/or the like that may enable auser to interact with the processing device. To facilitate segmentation,before the MRI device images the patient's brain, a contrast agent(e.g., a paramagnetic agent such as a gadolinium-based contrast agent,an agent including a dye/pigment, and/or the like) can be administeredto the patient such that the contrast agent enters the patient'sbloodstream and travels to the brain. Because the blood-brain barrier atbrain lesions may be compromised, a higher concentration of the contrastagent may be present at the brain lesion(s) compared to other regions inthe brain. As illustrated in the MRI images in FIGS. 3A and 3B, thisfacilitates the identification of brain lesion(s), which can exhibit ahigher intensity than other portions of the brain in the image and thuscan be identified as ROIs. In other embodiments, however, the first andsecond 3D representations can be obtained from non-contrast MRI imaging.

Each of the first and second 3D representations of the lesion can bedata that represents the geometry of the lesion (e.g., from which thevolume, surface area, and/or other geometric characteristics can becalculated). For example, referring to FIGS. 4A-4D—which each depictsorthographic projections of first and second 3D representations 62 a and62 b, respectively, of a lesion superimposed on one another—the firstand second 3D representations can each represent the lesion as apolyhedron whose surface is defined by a plurality of polygons (e.g.,triangles) (e.g., 66) and include data regarding the position of thepolygons' vertices (e.g., 70) in 3D coordinates (e.g., 3D Cartesiancoordinates) and/or the polygons' unit normals. As an illustration, thefirst and second 3D representations can each be a stereolithography(.stl) file representing the surface geometry of the lesion at the firstand second times, respectively. In other embodiments, however, the firstand second 3D representations can include any suitable data representingthe geometry of the lesion.

Some of the present methods include a step 18 of, from the first andsecond 3D representations, calculating one or more lesioncharacteristics of each of the lesion(s). The calculated lesioncharacteristic(s) can include: (1) a change in the lesion's volume fromthe first time to the second time, (2) a change in the lesion's surfacearea from the first time to the second time, (3) the lesion'sdisplacement from the first time to the second time, and/or (4) thetheoretical radius ratio of the lesion at at least one of, optionallyeach of, the first and second times. To do so, the processing system caninclude a 3D imaging application (e.g., 46) by which the processor(s)can calculate the lesion characteristic(s) from the first and second 3Drepresentations of the lesion.

To illustrate, when the first and second 3D representations eachrepresents the lesion as a polyhedron whose surface is defined by aplurality of triangles (e.g., when each is a stereolithography file),the lesion volume can be calculated by (1) for each of the triangles,calculating the signed volume of a tetrahedron having a base defined bythe triangle and a vertex at the origin and (2) summing the signedvolumes to determine the total lesion volume, and the lesion surfacearea can be calculated by summing the areas of the triangles. The changein the lesion volume and the lesion surface area can be determined bysubtracting, respectively, the volume and the surface area calculatedfrom the first 3D representation from the volume and the surface areacalculated from the second 3D representation. The change in the lesion'sposition can be calculated as the change in the position of the lesion'scentroid from the first time to the second time (e.g., from the firstand second 3D representations, respectively) and the displacement of thelesion can be calculated as the magnitude of the resulting vector.

The lesion theoretical radius ratio (R) can represent the extent towhich the lesion is spherical and, for each of the first and secondtimes, can be calculated as:

$\begin{matrix}{R = \frac{r_{SA}}{r_{V}}} & (1)\end{matrix}$

where r_(SA) is a theoretical radius of the lesion calculated from itssurface area at that time based on the assumption that the lesion isspherical:

$\begin{matrix}{r_{SA} = \sqrt{\frac{SA}{4\pi}}} & (2)\end{matrix}$

and r_(V) is a theoretical radius of the lesion calculated from itsvolume at that time based on the assumption that the lesion isspherical:

$\begin{matrix}{r_{V} = \sqrt[3]{\frac{V}{\frac{4}{3}\pi}}} & (3)\end{matrix}$

The closer R is to 1, the more spherical the lesion.

Some methods comprise a step 22 of characterizing whether the patienthas multiple sclerosis and/or the progression of multiple sclerosis inthe patient based at least in part on the calculation of the lesioncharacteristic(s) of each of the lesion(s). To do so, some methodsinclude assessing whether one or more criteria are satisfied, which canbe criteria whose assessment permits MS lesions to be distinguished fromNSWMD lesions and/or a characterization of the progression of MS. Forexample, the one or more criteria can include (1) a volume-basedcriterion that can be satisfied when the calculated change in thelesion's volume is less than or equal to a threshold volume change, (2)an area-based criterion that can be satisfied when the calculated changein the lesion's surface area is less than or equal to a thresholdsurface area change, (3) a displacement-based criterion that can besatisfied when the calculated displacement of the lesion is greater thanor equal to a threshold displacement, and/or (4) a deformation-basedcriterion that can be satisfied when the theoretical radius ratio of thelesion is greater than or equal to a threshold theoretical radius ratioat at least one of, optionally at each of, the first and second times.The processor(s) can be configured to perform the assessment of the oneor more criteria and/or characterize the presence and/or progression ofMS in the patient.

Satisfaction of the one or more criteria can indicate that the patienthas MS and/or that MS is progressing in the patient. For example, brainlesions in patients having MS may tend to have a slower growth in volumeand in surface area (and may even have a volume and a surface area thatdecrease with time) compared to those in patients having NSWMD, and thusa lesion having a change in volume and/or surface area that is lowerthan a threshold volume change and threshold surface area change,respectively, may be indicative of the presence and/or progression ofMS. Brain lesions in patients having MS may also experience greaterdisplacements compared to those in patients having NSWMD, and thus alesion having a displacement over time that is greater than a thresholddisplacement may also be indicative of the presence and/or progressionof MS. While the theoretical radius ratio of both MS lesions and NSWMDlesions may tend to remain substantially the same over time, NSWMDlesions may tend to be more spherical than MS lesions and, as such, alesion having a theoretical radius ratio above a threshold radius ratioat each of the first and second times may also be indicative of thepresence and/or progression of MS.

Referring to FIGS. 5A-5C and 6A-6C, for example, FIG. 5A shows a first3D representation 62 a of an MS lesion (empty mesh) with vectorsillustrating the position changes of the representation's vertices fromthe first time to the second time, FIG. 5B shows second 3Drepresentation 62 b of the MS lesion (filled mesh) in addition to thefirst representation (empty mesh) and the displacement vectors of FIG.5A, and FIG. 5C illustrates the displacement vectors of FIG. 5A alone;FIGS. 6A-6C show the same as FIGS. 5A-5C, respectively, but for an NSWMDlesion, with FIG. 6A illustrating the first 3D representation of theNSWMD lesion as an empty mesh and FIG. 6B illustrating the first andsecond 3D representations of the NSWMD lesion as filled and emptymeshes, respectively. As shown, over time the volume and the surfacearea of the MS lesion decrease while the volume and the surface area ofthe NSWMD lesion increases. Further, the displacement vectors—whenconsidering their magnitudes—are arranged more asymmetrically for the MSlesion compared to the NSWMD lesion, which may yield greaterdisplacement and/or deformation.

Due to the heterogeneity of MS lesions, a lesion need not satisfy allcriteria to be indicative of the presence and/or progression of MS(e.g., satisfaction of at least one of the one or more criteria may atleast in part support a finding that the patient has MS and/or that MSis progressing). When the brain includes multiple lesions, referencingeach of those lesions to determine the geometric characteristics thereofand assess the one or more criteria based on the calculatedcharacteristics may facilitate a more accurate characterization of thepresence and/or progression of MS despite this heterogeneity. With thepresence of more lesions satisfying more of the above-describedcriteria, it may be more likely that the patient has MS and/or that MSis progressing in the patient. For example, it can be determined thatthe patient has MS and/or that MS is progressing in the patient when forat least a majority of the lesions, such as greater than or equal to anyone of or between any two of 50%, 60%, 70%, 80%, or 90% (e.g., at least55%) of the lesions, at least one of the one or more criteria (e.g., thevolume-based criterion, the area-based criterion, the displacement-basedcriterion, and/or the deformation-based criterion) is satisfied. Toillustrate, and referring to FIG. 7 , shown is a 2D MRI image of a brainhaving multiple lesions superimposed with, for each of the lesions, afirst 3D representation 62 a (empty mesh) and a second 3D representation62 b (filled mesh) of the lesion taken at the first and second times,respectively. While some of the lesions exhibit different geometricchanges over time (e.g., with some exhibiting a reduction in volume andother exhibiting an increase in volume), the changes considered for alllesions may support an MS diagnosis.

The assessment of the one or more criteria can include determining thethreshold for at least one of the criteria, optionally based at least inpart on one or more patient-specific factors such as the age of thepatient, the time elapsed between the first and second times, and/or thelike. The processing system can include a database application (e.g.,50) comprising data regarding relevant thresholds—which may vary basedon the above factors—and the processor(s) can reference that data whenassessing whether the one or more criteria are satisfied. For example,for both MS lesions and NSWMD lesions, the change in volume and thechange in surface area may each tend to be larger for older patientsthan for younger patients and, as such, the threshold volume changeand/or the threshold surface area change can be determined based atleast in part on the age of the patient. To illustrate, for somepatients the threshold volume change can be less than or equal to anyone of, or between any two of, 2.0, 1.5, 1.0, 0.5, 0.0, −0.5, −1.0, or−1.5 cubic millimeters (mm³) (e.g., less than or equal to 1.5 mm³ orless than or equal to 0.0 mm³ (e.g., such that the lesion volumedecreases)) and/or the threshold surface area change can be less than orequal to any one of, or between any two of, 2.0, 1.5, 1.0, 0.5, 0.0,−0.5, −1.0, or −1.5 square millimeters (mm²) (e.g., less than or equalto 2.0 mm² or less than or equal to 0.0 mm² (e.g., such that the lesionsurface area decreases)). And, for some patients, the thresholddisplacement can be greater than or equal to any one of, or between anytwo of, 0.33, 0.35, 0.37, 0.39, 0.41, 0.43, 0.45, 0.47, or 0.49 mmand/or the threshold theoretical radius ratio can be greater than orequal to any one of, or between any two of, 1.02500, 1.02501, 1.02503,1.02504, 1.02505, 1.02506, 1.02507, 1.02508, 1.02509, 1.02600, 1.02601,1.02602, 1.02603, or 1.02604. At least some criteria may be assessedwithout referencing one or more patient-specific factors (e.g., becausesatisfaction of those criteria may be independent of the factor(s)); asan example, the assessment of the displacement criterion and/or thedeformation criterion can be performed without referencing the age ofthe patient (e.g., the threshold displacement and/or thresholdtheoretical radius ratio need not be determined based on the patient'sage).

The characterization need not be based on the assessment of thelesion(s) at the first and second times alone. For example, thecharacterization may further be based at least in part on 3Drepresentations of the lesion(s) obtained from data taken at othertimes, which similarly can be used to calculate changes in thegeometries of the lesion(s) (e.g., volume changes, surface area changes,displacement, and/or the theoretical radius ratio) over time that may beindicative of the presence and/or progression of MS.

Characterizing whether the patient has MS can include a determinationthat the patient has MS (e.g., if at least one of the criteria issatisfied for at least one, optionally a majority, of the lesion(s))and/or NSWMD. It can also include a determination of the patient's riskof having MS and/or NSWMD. Characterizing the progression of MS in thepatient can include a determination of whether—for a patient havingMS—MS is progressing or in remission. For example, some methods can beperformed to assess the efficacy of a treatment; in such methods, thetreatment (e.g., a medication) can be administered to the patient (e.g.,between the first and second times), where a determination that MS is inremission may indicate that the treatment is effective while adetermination that MS is not in remission may indicate that it is not.

The use of 3D—rather than 2D—representations of each of the lesion(s)taken at different points in time may promote the accurate diagnosis andassessment of MS and NSWMD. The time-based geometric characteristicsthat may vary between MS and NSWMD lesions (e.g., criteria based on thechange in volume, the change in surface area, displacement, and/ortheoretical radius ratio) may not be apparent in 2D representations of alesion, even for representations obtained from high-resolution MRIimages. However, these characteristics may be detectable from 3Drepresentations of a lesion such that they can be quantified andassessed. Because MS and NSWMD lesions may exhibit different changes inat least some of these geometric characteristics over time, theassessment based on 3D representations may permit more specific andaccurate diagnosis of a patient.

To illustrate, comparing FIGS. 3A and 3B—which each includes a 2D MRIaxial FLAIR image (left) taken at first and second times, respectively,of a brain having at least one lesion 58—with FIGS. 4A-4D—which eachshows first and second 3D representations 62 a and 62 b, respectively,of lesion 58 of FIGS. 3A and 3B—the 2D MRI images do not show adetectable change in the lesion's geometry, while the 3D representationsof the lesion show the lesion experiencing a reduction in volume and insurface area and a relatively large displacement, while maintaining anon-spherical shape. The changes in the 3D representations thus indicatethat the lesion may be an MS rather than an NSWMD lesion, something notdetectable on the 2D MRI images. Similarly, comparing FIGS. 8A and8B—which are also high-resolution 2D MRI axial FLAIR images taken atfirst and second times, respectively, of another brain having at leastone lesion 58—with FIGS. 9A-9D—which each shows first and second 3Drepresentations 62 a and 62 b, respectively, of lesion 58 of FIGS. 8Aand 8B—the 2D MRI images again do not show a detectable change in thelesion geometry while the 3D representations show that the lesionexperienced a relatively uniform growth in volume and in surface area,which may indicate that that the lesion may be an NSWMD rather than anMS lesion.

EXAMPLES

Aspects of the present invention will be described in greater detail byway of specific examples. The follow examples are offered forillustrative purposes only and are not intended to limit the inventionin any manner. Those of skill in the art will readily recognize avariety of noncritical parameters that can be changed or modified toyield essentially the same results.

Example 1

Brain lesions of 34 patients were analyzed using 3D MRI. The patientswere placed in two groups: (1) patients with a confirmed MS diagnosisbased on established criteria and results from supporting para-clinicalstudies (i.e., cerebrospinal fluid profiles, electrophysiological data,and/or serological results) to the exclusion of other disease states,and (2) patients with a history of brain anomalies atypical for in situdemyelination based on the observed radiological phenotype and formalimaging interpretations performed by a board-certified neuroradiologicaland clinical impressions from an MS specialist.

Imaging was performed with a 3T MRI scanner from Philips MedicalSystems, Cleveland OH, using a 32-channel phased array coil forreception and body coil for transmission. Each MRI study included scoutlocalizers, 3D high-resolution inversion recovery spoiled gradient-echoT1-weighted isotropic (1.0×1.0×1.0 mm³, TE/TR/TI=3.7/8.1/864 ms, flipangle 12 degrees, 256×220×170 mm³ FOV, number of excitations (NEX)=1,170slices, duration: 4:11 min), 3D fluid-attenuated inversion recovery(FLAIR) (1.1×1.1×1.1 mm³, TE/TR/TI=350/4800/1600 ms, flip angle 90degrees, 250×250×180 mm³ FOV, NEX=1,163 slices, duration: 5:02 min), and3D T2 sequence acquired in sagittal plane (1.0×1.0×1.0 mm³,TE/TR/TI=229/2500/1600 ms, flip angle 90 degrees, 250×250×180 mm³ FOV,NEX=1,164 slices, duration: 4:33 min). For each patient, MRI wasperformed at two points in time.

Lesions were segmented from the MRI images without knowledge of thepatients' clinical histories, current or past treatments, or diseasedurations. The selection of focal brain lesions with an area greaterthan 3 mm² within the supratentorial region were verified fromsimultaneously viewed 3D high-resolution FLAIR and T2-weightedsequences. The segmented lesions were saved as region of interest filesfor analysis. The mesh analysis software used for visualization of thelesions was ParaView from National Technology & Engineering Solutions ofSandia, LLC (NTESS) of Albuquerque, NM and Kitware Inc. of Clifton Park,NY. Over all 34 patients assessed, 405 lesions were segmented foranalysis: 248 from the MS group and 157 from the NSWMD group. The changein volume, change in surface area, and displacement from the first timeto the second time of each of the lesions were calculated. For each ofthe lesions, the theoretical radius ratio of the lesion at the firsttime (R₁) and at the second time (R₂) were also calculated. TABLE 1 setsforth the clinical data (e.g., patient characteristics) and lesion data(e.g., lesion characteristics calculated during the study) below. P₂₅and P₇₅ are the 25^(th) and 75^(th) percentiles, respectively.

TABLE 1 Clinical and Lesion Data for 3D MRI Longitudinal Study MultipleSclerosis Non-Specific White Group Matter Disease Group Clinical DataPatients 23 11 Mean Age (std. dev.) 42.2 years (11.9) 52.5 years (7.63)Female (% of patients) 14 (60.9%) 11 (100%) Race (% of patients) White21 (91.3%)  10 (90.9%) African American 2 (8.7%) 0 (0%)  Asian 0 (0%)   1 (9.1%) Hispanic (% of patients) 2 (8.7%)   2 (18.2%) Median DiseaseDuration (P₂₅, P₇₅) 1.99 years (0.54, 5.94) — Median Number Lesions(P₂₅, P₇₅)  11 (6.5, 14.5)  14 (12, 17) Lesion-Level Data LesionsAnalyzed 248 157 Median Duration Between MRI 1.65 years (1.26, 1.91) 2.74 years (1.72, 3.46)   Imaging (P₂₅, P₇₅) Median Change in Volume−2.32 mm³ (−9.67, 3.48) 3.94 mm³ (−0.65, 10.1) Between MRI Timepoints(P₂₅, P₇₅) Median Change in Surface Area −2.16 mm² (−8.19, 3.51) 4.18mm² (−0.63, 8.93) Between MRI Timepoints (P₂₅, P₇₅) Median ((R₁-1) ×100) (P₂₅, P₇₅) 3.38 (2.41, 4.63) 2.17 (1.54, 2.81) Median ((R_(ij2)-1)× 100) (P₂₅, P₇₅) 3.46 (2.48, 4.60) 2.10 (1.52, 3.06) MedianDisplacement (P₂₅, P₇₅)  0.39 mm (0.28, 0.56) 0.32 mm (0.22, 0.42)^(†)^(†)Based on n = 140.

To determine the differences between MS lesions and NSWMD lesions, astatistical analysis was performed using RStan in R to control fordifferences such as patient age and time between MRI studies. Forchanges in volume and in surface area, Bayesian linear mixed effectsregression models were constructed to fit the differences between the MSlesions' changes in volume and in surface area and the NSWMD lesions'changes in volume and in surface area. Random effects of the modelincluded subject-specific random intercepts to account for intra-subjectcorrelation; the random intercepts were assumed to be randomly sampledfrom a Student's t distribution because there was evidence of greaterkurtosis than that allowed by a normal distributions. The residuals werealso assumed to be randomly sampled from a Student's t distribution withdiagnosis-specific degrees-of-freedom parameters defined for both groupsbecause there was evidence of differing kurtosis and variability.

For displacement, the difference between the MS lesions' mediandisplacement and the NSWMD lesions' median displacement was alsoanalyzed with a Bayesian linear mixed effects regression model that wasfit to the log-transformed magnitude differences. Random effects of themodel included subject-specific random intercepts to account forintra-subject correlation between lesions; the random intercepts wereassumed to be randomly sampled from a normal distribution. The residualswere assumed to be randomly sampled from a Student's t distribution anddiagnosis-specific degrees-of-freedom parameters defined for bothgroups.

For theoretical radius ratio, a Bayesian linear mixed effects regressionmodel was also used to assess the difference in theoretical radiusratios between the MS lesions and the NSWMD lesions while controllingfor patient age, time elapsed between MRI studies, the interactionbetween patient age and time elapsed, and the interaction betweendiagnosis and time elapsed. Random effects of the model includedsubject-specific random intercepts to account for intra-subjectcorrelation and lesion-specific random intercepts to account forintra-lesion correlation. The subject-specific random intercepts wereassumed to be randomly sampled from a normal distribution.

For all of the Bayesian models used, the coefficients were given N(0,100) prior distributions. Degrees-of-freedom for all Student's tdistributions were given a Gamma (2, 0.1) prior distribution, allstandard deviation (or scale) parameters were given a Half-Cauchy (0,2.5) prior distribution, and the shape parameters for all Skew-Normaldistributions were given a N(0, 100) prior distribution. To ensure modelconvergence, three chains using 15,000 iterations with a 5,000 iterationburn-in were run and convergence was examined using the trace plots ofmodel parameters (FIGS. 10A-10B for lesion volume, FIGS. 11A-11B forlesion surface area, FIGS. 12A-12B for lesion displacement, and FIGS.13A-13C for lesion theoretical radius ratio). When convergence wasverified, the model was run with a single chain having 15,000 iterationswith a 5,000 iteration burn-in. To assess the distributional assumptionsfor the response, the sorted posterior mean of the residuals was plottedagainst the mean of the ordered posterior predictive residuals (FIG. 10Cfor lesion volume, FIG. 11C for lesion surface area, FIG. 12C for lesiondisplacement, and FIG. 13D for lesion theoretical radius ratio). A plotof the posterior mean of the random effects versus the mean of theposterior predictive random effects was also created (FIG. 10D forlesion volume, FIG. 11D for lesion surface area, FIG. 12D for lesiondisplacement, and FIGS. 13E and 13F for theoretical radius ratio). Aplot of the posterior mean of the residuals versus the posterior mean ofthe fitted values was generated to examine the homogeneity of varianceassumptions for models assuming normal or Student's t distributed errors(FIG. 10E for lesion volume, FIG. 11E for lesion surface area, FIG. 12Efor lesion displacement, and FIG. 13G for lesion theoretical radiusratio). Further, a plot of the prediction error generated based on theposterior predictive distribution versus the observed values wasgenerated (FIG. 10F for lesion volume, FIG. 11F for surface area, FIG.12F for displacement, and FIG. 13H for theoretical radius ratio).

The statistical analysis showed that the average change in lesion volumefor MS patients was 4.04 mm³ lower than that for NSWMD patients (95%Confidence Interval (CI)=(−7.90, −0.45), Bayesian two-sided p<0.05).With a one standard deviation increase in age, the average lesion volumechange for both MS and NSWMD patients increased by 1.95 mm³ (95%CI=(0.17, 3.66), Bayesian two-sided p<0.05), with the average for MSpatients being 0.49 mm³ and the average for NSWMD patients being 4.53mm³ at that age. As such, MS patients' lesions tended to experience lessvolume growth compared to those of the NSWMD patients.

The average change in lesion surface area for MS patients was 3.72 mm²lower than that of NSWMD patients (95% CI=(−7.46, 0.052), Bayesiantwo-sided p=0.05). With a one standard deviation increase in age, theaverage change in lesion surface area for both MS and NSWMD patientsincreased by 1.72 mm² (95% CI=(0.06, 3.42), Bayesian two-sided p<0.05),with the average for MS patients being 0.5 mm² and the average for NSWMDpatients being 4.22 mm². As such, MS patients' lesions tended toexperience less surface area growth compared to those of the NSWMDpatients (Bayesian two-sided p<0.05).

The difference in the average log of the median displacements betweenthe MS and NSWMD groups was 0.37 (95% CI=(0.08, 0.66), Bayesiantwo-sided p=0.01), meaning for an MS patient of average age with anaverage duration between MRI scans the median displacement was 44.8%greater (95% CI=(9.33%, 93.5%)) than that of an NSWMD patient,controlling for age and duration between MRI scans. The log of themedian displacement increased by 0.21 (95% CI=(0.08, 0.33), Bayesiantwo-sided p=0.0016) with a one standard deviation increase in the timeelapsed between the different MRI timepoints, indicating a 23.4%increase in displacement relative to the original position (95%CI=(8.33%, 39.1%)).

For MS patients, the theoretical radius ratio was 39.94% greater thanthat of the NSWD patients at the first timepoint (95% CI=(17.29%,66.23%), Bayesian two-sided p=0.0002) and 42.76% greater than that ofthe NSWMD patients at the second timepoint (95% CI=(19.72%, 68.51%),Bayesian two-sided p<0.0001), meaning that lesions from NSWMD patientstended to maintain a more spherical shape relative to those from MSpatients. The theoretical radius ratio for both MS patients and NSWMDpatients increased slightly between the first and second timepoints, butthe change was not statistically significant (Bayesian two-sidedp=0.291), indicating that the differences between the MS and NSWMDpatients were maintained over time.

The estimated diagnosis-specific scale parameters for thelesion-specific random effects were {circumflex over (σ)}_(NSWMD)=0.77(95% CI=(0.67, 0.89)) and {circumflex over (σ)}_(MS)=0.51(95% CI=(0.40,0.67)), which were significantly different (Bayesian two-sided p=0.006).The estimated shape parameters were {circumflex over (σ)}_(NSWMD)=12.14(95% CI=(4.18, 25.02)) and {circumflex over (σ)}_(MS)=0.65 (95%CI=(−1.31, 2.62)), indicating that the MS and NSWMD groups had differentshaper parameters (Bayesian two-sided p=0.0002), with the NSWMD groupbeing more skewered in the lesion-specific random effects. While theoverall variance of the lesion-specific random effects between the twodiagnoses were not statically different (Bayesian two-sided p=0.239),the difference in the shape parameters between the two diagnosesdemonstrated significantly less kurtosis for the MS patients (0.13, 95%CI=(−0.25, 0.61)) relative to the NSWMD patients (0.95, 95% CI=(0.81,0.99), Bayesian two-sided p=0.0002).

Example 2

A leave-one-out cross-validation was performed to assess a thresholdtheoretical radius ratio and threshold proportion of lesions satisfyingthe deformation-based criteria that may permit accurate MS and NSWMDdiagnoses. The data from Example 1 was separated into a test sampleconsisting of a single patient and a training sample consisting of theremainder of the patients. A logistic regression model was fit to thetrue diagnosis of a patient, with 0 corresponding to NSWMD and 1corresponding to MS. The proportion of lesions greater than a giventhreshold were treated as the independent variable using the trainingsample. The probability of each of the patients in the training samplebeing diagnosed with MS was computed and the Area Under the Curve (AUC)was computed for all possible thresholds. The threshold with the maximumAUC was then used and the proportion of lesions greater than thethreshold within each patient was computed. The logistic regressionmodel was again fit and the probability that each patient was diagnosedwith MS was computed for the training and testing sample. A ReceivingOperator Characteristics (ROC) curve was then constructed to determinethe probability cut-off which best differentiated the two diagnoses bychoosing the probability cut-off which had the maximum summation of thesensitivity and specificity. Based on this probability cut-off, thepredicted diagnosis was estimated for the patients in the training andtesting samples. Lastly, the classification error for the training andtesting samples were computed.

The results indicated that using a threshold theoretical radius ratio of1.02507 and a threshold proportion of lesions satisfying thedeformation-based criteria of 63%—meaning that an MS determination wouldbe made when at least 63% of the lesions have a theoretical radius ratioat at least one time—yielded a sample accuracy of 94.1% (e.g., in-sampleerror of 5.9%) and an out-of-sample accuracy of 88% (e.g., out-of-sampleerror of 12%).

The above specification and examples provide a complete description ofthe structure and use of illustrative embodiments. Although certainembodiments have been described above with a certain degree ofparticularity, or with reference to one or more individual embodiments,those skilled in the art could make numerous alterations to thedisclosed embodiments without departing from the scope of thisinvention. As such, the various illustrative embodiments of the methodsand systems are not intended to be limited to the particular formsdisclosed. Rather, they include all modifications and alternativesfalling within the scope of the claims, and embodiments other than theone shown may include some or all of the features of the depictedembodiment. For example, elements may be omitted or combined as aunitary structure, and/or connections may be substituted. Further, whereappropriate, aspects of any of the examples described above may becombined with aspects of any of the other examples described to formfurther examples having comparable or different properties and/orfunctions, and addressing the same or different problems. Similarly, itwill be understood that the benefits and advantages described above mayrelate to one embodiment or may relate to several embodiments.

The claims are not intended to include, and should not be interpreted toinclude, means-plus- or step-plus-function limitations, unless such alimitation is explicitly recited in a given claim using the phrase(s)“means for” or “step for,” respectively.

1. A method of analyzing one or more lesions of a brain of a patient,the method comprising: for each of the lesion(s), from a first3-dimensional (3D) representation of the lesion obtained from data takenat a first time and a second 3D representation of the lesion obtainedfrom data taken at a second time that is after the first time,calculating one or more lesion characteristics that include: a change,from the first time to the second time, in the volume of the lesion; achange, from the first time to the second time, in the surface area ofthe lesion; a displacement of the lesion from the first time to thesecond time; and/or the theoretical radius ratio of the lesion at eachof the first and second times; and characterizing whether the patienthas multiple sclerosis and/or the progression of multiple sclerosis inthe patient based at least in part on the calculation of the lesioncharacteristic(s) of each of the lesion(s).
 2. The method of claim 1,comprising: for each of the lesion(s), assessing whether one or morecriteria are satisfied, the one or more criteria including: adisplacement-based criterion that is satisfied when the displacement isgreater than or equal to a threshold displacement; whereincharacterizing whether the patient has multiple sclerosis and/or theprogression of multiple sclerosis in the patient is based at least inpart on the assessment of the one or more criteria for each of thelesion(s).
 3. The method of claim 1, comprising: for each of thelesion(s), assessing whether one or more criteria are satisfied, the oneor more criteria including: a deformation-based criterion that issatisfied when the theoretical radius ratio of the lesion at each of thefirst and second times is greater than or equal to a thresholdtheoretical radius ratio; wherein characterizing whether the patient hasmultiple sclerosis and/or the progression of multiple sclerosis in thepatient is based at least in part on the assessment of the one or morecriteria for each of the lesion(s).
 4. The method of claim 2,comprising: determining for at least a majority of the lesion(s) that atleast one of the one or more criteria is satisfied; and determining thatthe patient has multiple sclerosis.
 5. The method of claim 3,comprising: determining for at least a majority of the lesion(s) that atleast one of the one or more criteria is satisfied; and determining thatthe patient has multiple sclerosis.
 6. The method of claim 2, wherein:the threshold displacement is not determined based on the age of thepatient.
 7. The method of claim 3, wherein: the threshold theoreticalradius ratio is not determined based on the age of the patient.
 8. Themethod of claim 1, wherein the data taken at the first time and the datataken at the second time each is a 3D magnetic resonance imaging (MRI)image of the brain of the patient.
 9. The method of claim 2, wherein thedata taken at the first time and the data taken at the second time eachis a 3D magnetic resonance imaging (MRI) image of the brain of thepatient.
 10. The method of claim 3, wherein the data taken at the firsttime and the data taken at the second time each is a 3D magneticresonance imaging (MRI) image of the brain of the patient.
 11. Themethod of claim 1, wherein the time elapsed between the first and secondtimes is between 6 months and 4 years.
 12. A system for analyzing one ormore lesions of a brain of a patient, the system comprising one or moreprocessors configured to: for each of the lesion(s), from a first3-dimensional (3D) representation of the lesion obtained from data takenat a first time and a second 3D representation of the lesion obtainedfrom data taken at a second time that is after the first time, calculateone or more lesion characteristics that include: a change, from thefirst time to the second time, in the volume of the lesion; a change,from the first time to the second time, in the surface area of thelesion; a displacement of the lesion from the first time to the secondtime; and/or the theoretical radius ratio of the lesion at each of thefirst and second times; and characterize whether the patient hasmultiple sclerosis and/or the progression of multiple sclerosis in thepatient based at least in part on the calculation of the lesioncharacteristic(s) of each of the lesion(s).
 13. The system of claim 12,wherein the processor(s) are configured to: for each of the lesion(s),assess whether one or more criteria are satisfied, the one or morecriteria including: a displacement-based criterion that is satisfiedwhen the change in the position of the lesion is greater than or equalto a threshold displacement; and characterize whether the patient hasmultiple sclerosis and/or the progression of multiple sclerosis in thepatient based at least in part on the assessment of the one or morecriteria for each of the lesion(s).
 14. The system of claim 12, whereinthe processor(s) are configured to: for each of the lesion(s), assesswhether one or more criteria are satisfied, the one or more criteriaincluding: a deformation-based criterion that is satisfied when thetheoretical radius ratio of the lesion at each of the first and secondtimes is greater than or equal to a threshold theoretical radius ratio;and characterize whether the patient has multiple sclerosis and/or theprogression of multiple sclerosis in the patient based at least in parton the assessment of the one or more criteria for each of the lesion(s).15. The system of claim 13, wherein: the processor(s) are configured toassess whether the displacement-based criterion is satisfied withoutdetermining the threshold displacement based on the age of the patient.16. The system of claim 14, wherein: the processor(s) are configured toassess whether the deformation-based criterion is satisfied withoutdetermining the threshold theoretical radius ratio based on the age ofthe patient.
 17. The system of claim 12, wherein the data taken at thefirst time and the data taken at the second time each is a 3D magneticresonance imaging (MRI) image of the brain of the patient.